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Guided Learning for Bidirectional Sequence Classification

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This week Yue will be talking about:

Guided Learning for Bidirectional Sequence Classification

In this paper, we propose guided learning, a new learning framework for bidirectional sequence classification. The tasks of learning the order of inference and training the local classifier are dynamically incorporated into a single Perceptron like learning algorithm. We apply this novel learning algorithm to POS tagging. It obtains an error rate of 2.67% on the standard PTB test set, which represents 3.3% relative error reduction over the previous best result on the same data set, while using fewer features.

This talk is part of the Natural Language Processing Reading Group series.

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